%-------------------------------------------------------------------------------
% UF Sparse Matrix Collection, Tim Davis
% http://www.cise.ufl.edu/research/sparse/matrices/SNAP/Oregon-2
% name: SNAP/Oregon-2
% [(9 graphs) AS peering info inferred from Oregon route-views, 3/31-5/26/01]
% id: 2324
% date: 2001
% author: J. Leskovec, J. Kleinberg and C. Faloutsos
% ed: J. Leskovec
% fields: name title A id date author ed kind notes aux
% aux: G Gname nodename
% kind: undirected graph sequence
%-------------------------------------------------------------------------------
% notes:
% Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets,     
% Jure Leskovec http://snap.stanford.edu/data/index.html                         
% email jure at cs.stanford.edu                                                  
%                                                                                
% Autonomous systems - Oregon-2                                                  
%                                                                                
% Dataset information                                                            
%                                                                                
% 9 Autonomous systems graphs, 1 per week between March 31 2001 and May 26 2001. 
% Graphs represent AS peering information inferred from Oregon route-views,      
% Looking glass data, and Routing registry, all combined.                        
%                                                                                
% Dataset statistics are calculated for the graph with the lowest (March 31 2001)
% and highest (from May 26 2001) number of nodes:                                
%                                                                                
% Dataset statistics for graph with lowest number of nodes - 3 31 2001           
%                                                                                
% Nodes   10900                                                                  
% Edges   31180                                                                  
% Nodes in largest WCC    10900 (1.000)                                          
% Edges in largest WCC    31180 (1.000)                                          
% Nodes in largest SCC    10900 (1.000)                                          
% Edges in largest SCC    31180 (1.000)                                          
% Average clustering coefficient  0.5009                                         
% Number of triangles     82856                                                  
% Fraction of closed triangles    0.03855                                        
% Diameter (longest shortest path)    9                                          
% 90-percentile effective diameter    4.3                                        
%                                                                                
% Dataset statistics for graph with highest number of nodes - 5 26 2001          
%                                                                                
% Nodes   11461                                                                  
% Edges   32730                                                                  
% Nodes in largest WCC    11461 (1.000)                                          
% Edges in largest WCC    32730 (1.000)                                          
% Nodes in largest SCC    11461 (1.000)                                          
% Edges in largest SCC    32730 (1.000)                                          
% Average clustering coefficient  0.4943                                         
% Number of triangles     89541                                                  
% Fraction of closed triangles    0.03701                                        
% Diameter (longest shortest path)    9                                          
% 90-percentile effective diameter    4.3                                        
%                                                                                
% Source (citation)                                                              
%                                                                                
% J. Leskovec, J. Kleinberg and C. Faloutsos. Graphs over Time: Densification    
% Laws, Shrinking Diameters and Possible Explanations. ACM SIGKDD International  
% Conference on Knowledge Discovery and Data Mining (KDD), 2005.                 
%                                                                                
% Files                                                                          
% File    Description                                                            
%         AS peering information inferred from Oregon route-views, Looking glass 
%         data, and Routing registry,  ...                                       
% oregon2_010331.txt.gz from March 31 2001                                       
% oregon2_010407.txt.gz from April 7 2001                                        
% oregon2_010414.txt.gz from April 14 2001                                       
% oregon2_010421.txt.gz from April 21 2001                                       
% oregon2_010428.txt.gz from April 28 2001                                       
% oregon2_010505.txt.gz from May 05 2001                                         
% oregon2_010512.txt.gz from May 12 2001                                         
% oregon2_010519.txt.gz from May 19 2001                                         
% oregon2_010526.txt.gz from May 26 2001                                         
%                                                                                
% NOTE: for the UF Sparse Matrix Collection, the primary matrix in this problem  
% set (Problem.A) is the last matrix in the sequence, oregon2_010526, from May 26
% 2001.                                                                          
%                                                                                
% The nodes are uniform across all graphs in the sequence in the UF collection.  
% That is, nodes do not come and go.  A node that is "gone" simply has no edges. 
% This is to allow comparisons across each node in the graphs.                   
% Problem.aux.nodenames gives the node numbers of the original problem.  So      
% row/column i in the matrix is always node number Problem.aux.nodenames(i) in   
% all the graphs.                                                                
%                                                                                
% Problem.aux.G{k} is the kth graph in the sequence.                             
% Problem.aux.Gname(k,:) is the name of the kth graph.                           
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